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Denis Peskoff
Denis Peskoff
Postdoctoral Researcher at Princeton University
Verified email at princeton.edu - Homepage
Title
Cited by
Cited by
Year
Can you unpack that? learning to rewrite questions-in-context
A Elgohary, D Peskov, J Boyd-Graber
Can You Unpack That? Learning to Rewrite Questions-in-Context, 2019
2022019
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence
A Hoyle, P Goel, A Hian-Cheong, D Peskov, J Boyd-Graber, P Resnik
Advances in Neural Information Processing Systems 34, 2021
952021
Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data
D Peskov, N Clarke, J Krone, B Fodor, Y Zhang, A Youssef, M Diab
Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019
522019
It Takes Two to Lie: One to Lie, and One to Listen
D Peskov, B Cheng, A Elgohary, J Barrow, C Danescu-Niculescu-Mizil, ...
362020
Mitigating noisy inputs for question answering
D Peskov, J Barrow, P Rodriguez, G Neubig, J Boyd-Graber
arXiv preprint arXiv:1908.02914, 2019
132019
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation
D Stojanovski, B Krojer, D Peskov, A Fraser
Proceedings of the 28th International Conference on Computational …, 2020
122020
Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
E Redmiles, L Maszkiewicz, E Hwang, D Kuchhal, E Liu, M Morales, ...
Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019
122019
Credible without Credit: Domain Experts Assess Generative Language Models
D Peskoff, BM Stewart
Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023
102023
UMDeep at SemEval-2017 Task 1: End-to-end shared weight LSTM model for semantic textual similarity
J Barrow, D Peskov
Proceedings of the 11th International Workshop on Semantic Evaluation …, 2017
82017
Adapting Entities across Languages and Cultures
D Peskov, V Hangya, J Boyd-Graber, A Fraser
Findings of the Association for Computational Linguistics: EMNLP 2021, 3725-3750, 2021
62021
Measuring the Rapid Growth of HSTS and HPKP Deployments
I Petrov, D Peskov, G Coard, T Chung, D Choffnes, D Levin, BM Maggs, ...
2
GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves
D Peskoff, A Visokay, S Schulhoff, B Wachspress, A Blinder, BM Stewart
Findings of the Association for Computational Linguistics: EMNLP 2023, 6529-6539, 2023
2023
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